Analysis on World Happiness Dataset
- Initial Exploration of Dataset
- Characteristics of the Dataset
Identifying Happiest and Unhappiest Countries and Regions
Exploring Happiness Rank
Exploring Happiness Score based on Region
What factors have strong relationship with Happiness?
- Correlogram
- Heatmap
Strong Positive Correlations (corr>+0.7) with Happiness Score
Happiest Regions vs. Unhappiest Region
Happiest Regions

Analysis on World Happiness Dataset

In this project I will analyze World Happiness dataset, which has 155 countries and different attributes. I took the dataset from Kaggle: https://www.kaggle.com/unsdsn/world-happiness/home

Initial Exploration of Dataset

I use “Import Dataset” at the Global Environment to load the dataset. Then I convert the dataset to dataframe.

library(kableExtra)
library(knitr)
library(readr)
X2015 <- read_csv("~/Desktop/world-happiness-report/2015.csv")
happy_2015 <- as.data.frame(X2015)

I use some functions to have a glance at the dataset and understand it. I would like to check the column(attribute) names initially, to see if I should make any changes before starting to further explore the dataset. I like to change the names, optimize them in a meaningful and shorter manner. This way it makes it easier for me to call column names, in the future.

#Viewing column names
names(happy_2015)

##  [1] "Country"                       "Region"                       
##  [3] "Happiness Rank"                "Happiness Score"              
##  [5] "Standard Error"                "Economy (GDP per Capita)"     
##  [7] "Family"                        "Health (Life Expectancy)"     
##  [9] "Freedom"                       "Trust (Government Corruption)"
## [11] "Generosity"                    "Dystopia Residual"

It would be better to change all column names in a way that they won’t include white space in their names. It is also good to note their original name in the metadata, in case the attribute name we create is not clear enough. I keep them just to be on the safe side :)

#Changing column names
names(happy_2015) <- c("country", "region", "happiness_rank", "happiness_score", "std_error", "gdp_per_cpt", "family", "life_exp", "freedom", "trust_corruption", "generosity", "dystopia_residual")

Characteristics of the Dataset

Now we can start exploring the dataset. I will look at the head of the data set to see the happiest countries.

kable(head(happy_2015[, c("country", "region", "happiness_rank", "happiness_score")]))%>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	region	happiness_rank	happiness_score
Switzerland	Western Europe	1	7.587
Iceland	Western Europe	2	7.561
Denmark	Western Europe	3	7.527
Norway	Western Europe	4	7.522
Canada	North America	5	7.427
Finland	Western Europe	6	7.406

I run initial summary statistics to have a general idea about the observaions. Also I look at the total number of missing values in the data.

#Viewing stats about each attribute
summary(happy_2015)

#structure
str(happy_2015)

#missing values 
sum(is.na(happy_2015))

library(fBasics) #library for the summary table

#subsetting the dataset to only include numeric values
num_hap <- happy_2015[, c("happiness_rank", "happiness_score", "std_error", "gdp_per_cpt", "family", "life_exp", "freedom", "trust_corruption", "generosity", "dystopia_residual")]

#subsetting the summary table to view only stats below
basicStats(num_hap)[c("Mean", "Stdev", "Median", "Minimum", "Maximum"),]

##         happiness_rank happiness_score std_error gdp_per_cpt   family
## Mean          79.49367        5.375734  0.047885    0.846137 0.991046
## Stdev         45.75436        1.145010  0.017146    0.403121 0.272369
## Median        79.50000        5.232500  0.043940    0.910245 1.029510
## Minimum        1.00000        2.839000  0.018480    0.000000 0.000000
## Maximum      158.00000        7.587000  0.136930    1.690420 1.402230
##         life_exp  freedom trust_corruption generosity dystopia_residual
## Mean    0.630259 0.428615         0.143422   0.237296          2.098977
## Stdev   0.247078 0.150693         0.120034   0.126685          0.553550
## Median  0.696705 0.435515         0.107220   0.216130          2.095415
## Minimum 0.000000 0.000000         0.000000   0.000000          0.328580
## Maximum 1.025250 0.669730         0.551910   0.795880          3.602140

Our data seems pretty much clean. Additionally, there are no missing values in our dataset, which will accelerate our analysis. We don’t have to put any time to handle the missing values.

I also want to count how many countries and regions we have:

#Number of countries in our dataset
length(unique(happy_2015$country))

## [1] 158

#Number of regions
length(unique(happy_2015$region))

## [1] 10

Since we have 10 regions and 158 countries, we can subset our dataset to regions and look at the distributions. By analyzing regions separetely, we can find out about their characteristics. But before zooming into the regions(subsetting), we can have a look at the world in general.

Identifying Happiest and Unhappiest Countries and Regions

Exploring Happiness Rank

Rank=1 indicates the happiest country in the world.Keeping that in the mind we can have a look the first 10 and last ten countries in the list. Top ten happiest countries:

kable(head(happy_2015, 10)) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	region	happiness_rank	happiness_score	std_error	gdp_per_cpt	family	life_exp	freedom	trust_corruption	generosity	dystopia_residual
Switzerland	Western Europe	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
Iceland	Western Europe	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
Denmark	Western Europe	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
Norway	Western Europe	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
Canada	North America	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176
Finland	Western Europe	6	7.406	0.03140	1.29025	1.31826	0.88911	0.64169	0.41372	0.23351	2.61955
Netherlands	Western Europe	7	7.378	0.02799	1.32944	1.28017	0.89284	0.61576	0.31814	0.47610	2.46570
Sweden	Western Europe	8	7.364	0.03157	1.33171	1.28907	0.91087	0.65980	0.43844	0.36262	2.37119
New Zealand	Australia and New Zealand	9	7.286	0.03371	1.25018	1.31967	0.90837	0.63938	0.42922	0.47501	2.26425
Australia	Australia and New Zealand	10	7.284	0.04083	1.33358	1.30923	0.93156	0.65124	0.35637	0.43562	2.26646

The happiest counties are mostly in Western Europe.

Least ten happiest countries:

kable(tail(happy_2015, 10)) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

	country	region	happiness_rank	happiness_score	std_error	gdp_per_cpt	family	life_exp	freedom	trust_corruption	generosity	dystopia_residual
149	Chad	Sub-Saharan Africa	149	3.667	0.03830	0.34193	0.76062	0.15010	0.23501	0.05269	0.18386	1.94296
150	Guinea	Sub-Saharan Africa	150	3.656	0.03590	0.17417	0.46475	0.24009	0.37725	0.12139	0.28657	1.99172
151	Ivory Coast	Sub-Saharan Africa	151	3.655	0.05141	0.46534	0.77115	0.15185	0.46866	0.17922	0.20165	1.41723
152	Burkina Faso	Sub-Saharan Africa	152	3.587	0.04324	0.25812	0.85188	0.27125	0.39493	0.12832	0.21747	1.46494
153	Afghanistan	Southern Asia	153	3.575	0.03084	0.31982	0.30285	0.30335	0.23414	0.09719	0.36510	1.95210
154	Rwanda	Sub-Saharan Africa	154	3.465	0.03464	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628	0.67042
155	Benin	Sub-Saharan Africa	155	3.340	0.03656	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260	1.63328
156	Syria	Middle East and Northern Africa	156	3.006	0.05015	0.66320	0.47489	0.72193	0.15684	0.18906	0.47179	0.32858
157	Burundi	Sub-Saharan Africa	157	2.905	0.08658	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727	1.83302
158	Togo	Sub-Saharan Africa	158	2.839	0.06727	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681	1.56726

Majority of the unhappiest countries are located in Sub-Saharan Africa. Countries which are not located in Sub-Saharan Africa and still included in this list are Syria and Afghanistan, which is not suprising if we consider the ongoing war and terrorism in those countries.

Since happiness rank is based on happiness score, I would like to analyze the average happiness score among regions and then try to focus on the characteristics of the happiest and unhappiest regions.

Exploring Happiness Score based on Region

library(dplyr)
library(tidyr)
library(plotly)

#table with average happiness per region
avg_happiness_region <-happy_2015 %>%
        group_by(region) %>%          
        summarise(avg_happiness = mean(happiness_score, round(1)))


#Plotting the average happiness scores to compare regions
p_avg_happiness_region <- plot_ly(avg_happiness_region, x = ~region,
                                  y = ~avg_happiness, 
                                  type = 'bar', 
                                  name = 'Average Happiness') %>% 
  #add_trace(y = ~mean(happy_2015_copy$happiness_score), name = 'world')%>%
  layout(title="Average Happiness per Region in 2015")
htmltools::tagList(list(p_avg_happiness_region))

Top 3 happiest regions based on average happiness score are: -Australia & New Zealand -North America -Western Europe

It is important to note that both of the first two regions include only 2 countries and Western Europe has 21 countries. Additionally all of these regions include countries with developed economies.

The unhappiest region is Sub-Saharan Africa, which includes 40 different countries.

What factors have strong relationship with Happiness?

Now we can create a correlogram to analyze the relationships. To do that we need only numeric columns. So I select the numeric columns only and create a dataframe called num_hap.

#names(happy_2015)
num_hap <- happy_2015[, c("happiness_rank", "happiness_score", "std_error", "gdp_per_cpt", "family", "life_exp", "freedom", "trust_corruption", "generosity", "dystopia_residual")]

Correlogram

library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(num_hap)

## $r
##                   happiness_rank std_error generosity freedom
## happiness_rank                 1                             
## std_error                   0.16         1                   
## generosity                 -0.16    -0.088          1        
## freedom                    -0.56     -0.13       0.37       1
## trust_corruption           -0.37     -0.18       0.28    0.49
## dystopia_residual          -0.52     0.084       -0.1   0.063
## gdp_per_cpt                -0.79     -0.22      -0.01    0.37
## life_exp                   -0.74     -0.31       0.11    0.36
## happiness_score            -0.99     -0.18       0.18    0.57
## family                     -0.73     -0.12      0.088    0.44
##                   trust_corruption dystopia_residual gdp_per_cpt life_exp
## happiness_rank                                                           
## std_error                                                                
## generosity                                                               
## freedom                                                                  
## trust_corruption                 1                                       
## dystopia_residual           -0.033                 1                     
## gdp_per_cpt                   0.31              0.04           1         
## life_exp                      0.25             0.019        0.82        1
## happiness_score                0.4              0.53        0.78     0.72
## family                        0.21              0.15        0.65     0.53
##                   happiness_score family
## happiness_rank                          
## std_error                               
## generosity                              
## freedom                                 
## trust_corruption                        
## dystopia_residual                       
## gdp_per_cpt                             
## life_exp                                
## happiness_score                 1       
## family                       0.74      1
## 
## $p
##                   happiness_rank std_error generosity freedom
## happiness_rank                 0                             
## std_error                  0.047         0                   
## generosity                 0.044      0.27          0        
## freedom                    3e-14       0.1    1.3e-06       0
## trust_corruption         1.5e-06     0.025    0.00045 4.4e-11
## dystopia_residual          2e-12      0.29       0.21    0.43
## gdp_per_cpt              2.7e-34     0.006        0.9 1.7e-06
## life_exp                 3.5e-28   7.3e-05       0.18 3.3e-06
## happiness_score         1.4e-142     0.026      0.023 6.9e-15
## family                   5.8e-28      0.13       0.27 6.4e-09
##                   trust_corruption dystopia_residual gdp_per_cpt life_exp
## happiness_rank                                                           
## std_error                                                                
## generosity                                                               
## freedom                                                                  
## trust_corruption                 0                                       
## dystopia_residual             0.68                 0                     
## gdp_per_cpt                8.3e-05              0.62           0         
## life_exp                    0.0017              0.81     4.8e-39        0
## happiness_score            2.8e-07           7.6e-13     1.1e-33  5.8e-27
## family                      0.0096             0.063     5.6e-20    7e-13
##                   happiness_score family
## happiness_rank                          
## std_error                               
## generosity                              
## freedom                                 
## trust_corruption                        
## dystopia_residual                       
## gdp_per_cpt                             
## life_exp                                
## happiness_score                 0       
## family                    9.9e-29      0
## 
## $sym
##                   happiness_rank std_error generosity freedom
## happiness_rank    1                                          
## std_error                        1                           
## generosity                                 1                 
## freedom           .                        .          1      
## trust_corruption  .                                   .      
## dystopia_residual .                                          
## gdp_per_cpt       ,                                   .      
## life_exp          ,              .                    .      
## happiness_score   B                                   .      
## family            ,                                   .      
##                   trust_corruption dystopia_residual gdp_per_cpt life_exp
## happiness_rank                                                           
## std_error                                                                
## generosity                                                               
## freedom                                                                  
## trust_corruption  1                                                      
## dystopia_residual                  1                                     
## gdp_per_cpt       .                                  1                   
## life_exp                                             +           1       
## happiness_score   .                .                 ,           ,       
## family                                               ,           .       
##                   happiness_score family
## happiness_rank                          
## std_error                               
## generosity                              
## freedom                                 
## trust_corruption                        
## dystopia_residual                       
## gdp_per_cpt                             
## life_exp                                
## happiness_score   1                     
## family            ,               1     
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1

Heatmap

Another way to show the correlation, with a heatmap. But I prefer the correlagram, as I find it easier to read.

cormat<-rquery.cormat(num_hap, graphType="heatmap")

Now we can focus on the attributes that have strong correlation with happiness score.

Strong Positive Correlations (corr>+0.7) with Happiness Score

Strong correlations will give me an idea about which factors were more related to happiness score. It is always important to note that correlation and causation are different things!

* Gdp Per Capita

cor(happy_2015$happiness_score, happy_2015$gdp_per_cpt)

## [1] 0.7809655

Countries that have higher gdp per capita seems to be happier. Since higher gdp per capita indicates a better standard of living for a country, it makes sense.

ggplot(happy_2015, aes(x=happy_2015$gdp_per_cpt, y=happy_2015$happiness_score))+ 
  geom_point(aes(color = happy_2015$region)) +
  geom_smooth(method="lm") + 
  xlab("GDP per Capita") + 
  ylab("Happiness Score") + 
  labs(colour="Region")

* Family

cor(happy_2015$happiness_score, happy_2015$family)

## [1] 0.7406052

Although the explanation family attribute is not clear in the metadata, family contributes to happier people.

ggplot(happy_2015, aes(x=happy_2015$family, y=happy_2015$happiness_score))+ geom_point(aes(color = happy_2015$region)) +
geom_smooth(method="lm") + 
  xlab("Family") + 
  ylab("Happiness Score") + 
  labs(colour="Region")

* Life Expectancy

cor(happy_2015$happiness_score, happy_2015$life_exp)

## [1] 0.7241996

Life expectancy, probably the average years of life contributes to happier positively.

ggplot(happy_2015, aes(x=happy_2015$life_exp, y=happy_2015$happiness_score))+ geom_point(aes(color = happy_2015$region)) +
geom_smooth(method="lm") + 
  xlab("Lİfe Expectancy") + 
  ylab("Happiness Score") + 
  labs(colour="Region")

* Freedom

cor(happy_2015$happiness_score, happy_2015$freedom)

## [1] 0.5682109

Freedom doesn’t have a very strong relationship as life expectancy, gdp per capita and family.

Happiest Regions vs. Unhappiest Region

To make a comparison between regions, or to analyze each region separetely in the future, I subset the dataset according to the region. ##Subsetting our dataset Now we have subsetted the dataset into the regions we can do analysis on each region.

unique(happy_2015$region)

##  [1] "Western Europe"                  "North America"                  
##  [3] "Australia and New Zealand"       "Middle East and Northern Africa"
##  [5] "Latin America and Caribbean"     "Southeastern Asia"              
##  [7] "Central and Eastern Europe"      "Eastern Asia"                   
##  [9] "Sub-Saharan Africa"              "Southern Asia"

#############################
#Happiest Regions
#############################

#Australia & New Zealand
aust_newzealand <- happy_2015[which(happy_2015$region == "Australia and New Zealand"), ]

#Subsetting Western Europe
w_europe <- happy_2015[which(happy_2015$region == "Western Europe"), ]

#North America
n_america <- happy_2015[which(happy_2015$region == "North America"), ]

#Happiest regions Altogether
happy_regions <- rbind(aust_newzealand, w_europe, n_america)


#############################
#  Unhappiest Region
#############################
#Sub-Saharan Africa
sub_saharan_africa <- happy_2015[which(happy_2015$region == "Sub-Saharan Africa"), ]


#############################
# Other Regions
#############################
#Latin America & Caribbean
l_america <- happy_2015[which(happy_2015$region == "Latin America and Caribbean"), ]
#Middle East and Northern Africa
m_east_n_africa <- happy_2015[which(happy_2015$region == "Middle East and Northern Africa"), ]
#Central and Eastern Europe
central_easteu <- happy_2015[which(happy_2015$region == "Central and Eastern Europe"), ]
#Eastern Asia
east_asia <- happy_2015[which(happy_2015$region == "Eastern Asia"), ]
#Southern Asia
south_asia <- happy_2015[which(happy_2015$region == "Southern Asia"), ]

Happiest Regions

After subsetting the happiest regions as Aust. & New Zealand, Western Europe and North America, we have 25 countries.

Distributions with Histogram

library(ggplot2)

#subsetting numeric columns for drawing a histogram
num_hap_regions <- happy_regions[, c("happiness_rank", "happiness_score", "std_error", "gdp_per_cpt", "family", "life_exp", "freedom", "trust_corruption", "generosity", "dystopia_residual")] 

#iterating through every column and plotting a histogram
column_names <- colnames(num_hap_regions)
for (i in 1:ncol(num_hap_regions)) {
  (ggplot(num_hap_regions, aes(num_hap_regions[,i])) + 
        geom_histogram(fill="gray") + 
          xlab(column_names[i]))}
par(mfrow = c(5,2))

I didn’t wanted to display the histograms individually, as I want to zoom in to sum attributes specifically. However, you can run the code and look at the distributions.

num_hap_regions <- happy_regions[, c("happiness_rank", "happiness_score", "std_error", "gdp_per_cpt", "family", "life_exp", "freedom", "trust_corruption", "generosity", "dystopia_residual")]
rquery.cormat(num_hap_regions)

## $r
##                   happiness_score dystopia_residual gdp_per_cpt
## happiness_score                 1                              
## dystopia_residual            0.85                 1            
## gdp_per_cpt                  0.69              0.53           1
## trust_corruption             0.81              0.56        0.61
## generosity                   0.61              0.28        0.28
## family                       0.82               0.6        0.47
## freedom                      0.82              0.47        0.56
## life_exp                  -0.0076             0.044      -0.048
## happiness_rank              -0.99             -0.82       -0.69
## std_error                   -0.55             -0.38       -0.39
##                   trust_corruption generosity family freedom life_exp
## happiness_score                                                      
## dystopia_residual                                                    
## gdp_per_cpt                                                          
## trust_corruption                 1                                   
## generosity                    0.46          1                        
## family                        0.59       0.52      1                 
## freedom                       0.73       0.63   0.77       1         
## life_exp                     -0.13       -0.1  0.041   -0.12        1
## happiness_rank               -0.79      -0.62  -0.86   -0.83   -0.039
## std_error                    -0.56       -0.3  -0.61   -0.47     0.11
##                   happiness_rank std_error
## happiness_score                           
## dystopia_residual                         
## gdp_per_cpt                               
## trust_corruption                          
## generosity                                
## family                                    
## freedom                                   
## life_exp                                  
## happiness_rank                 1          
## std_error                   0.59         1
## 
## $p
##                   happiness_score dystopia_residual gdp_per_cpt
## happiness_score                 0                              
## dystopia_residual         6.8e-08                 0            
## gdp_per_cpt               0.00013            0.0066           0
## trust_corruption            1e-06             0.004      0.0013
## generosity                 0.0012              0.17        0.17
## family                    4.1e-07            0.0015       0.017
## freedom                   5.2e-07             0.017      0.0036
## life_exp                     0.97              0.84        0.82
## happiness_rank            3.6e-22           4.9e-07     0.00012
## std_error                  0.0042             0.058       0.054
##                   trust_corruption generosity  family freedom life_exp
## happiness_score                                                       
## dystopia_residual                                                     
## gdp_per_cpt                                                           
## trust_corruption                 0                                    
## generosity                    0.02          0                         
## family                      0.0019     0.0075       0                 
## freedom                    2.9e-05    0.00074 7.7e-06       0         
## life_exp                      0.54       0.62    0.85    0.57        0
## happiness_rank             3.3e-06    0.00089 5.1e-08 3.6e-07     0.85
## std_error                   0.0038       0.14  0.0012   0.018      0.6
##                   happiness_rank std_error
## happiness_score                           
## dystopia_residual                         
## gdp_per_cpt                               
## trust_corruption                          
## generosity                                
## family                                    
## freedom                                   
## life_exp                                  
## happiness_rank                 0          
## std_error                 0.0019         0
## 
## $sym
##                   happiness_score dystopia_residual gdp_per_cpt
## happiness_score   1                                            
## dystopia_residual +               1                            
## gdp_per_cpt       ,               .                 1          
## trust_corruption  +               .                 ,          
## generosity        ,                                            
## family            +               .                 .          
## freedom           +               .                 .          
## life_exp                                                       
## happiness_rank    B               +                 ,          
## std_error         .               .                 .          
##                   trust_corruption generosity family freedom life_exp
## happiness_score                                                      
## dystopia_residual                                                    
## gdp_per_cpt                                                          
## trust_corruption  1                                                  
## generosity        .                1                                 
## family            .                .          1                      
## freedom           ,                ,          ,      1               
## life_exp                                                     1       
## happiness_rank    ,                ,          +      +               
## std_error         .                           ,      .               
##                   happiness_rank std_error
## happiness_score                           
## dystopia_residual                         
## gdp_per_cpt                               
## trust_corruption                          
## generosity                                
## family                                    
## freedom                                   
## life_exp                                  
## happiness_rank    1                       
## std_error         .              1        
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1

Happiest regions on the map

library(tmap)

Comparison between Happy and Unhappy Regions

ggplot(happy_regions, aes(x=happy_regions$gdp_per_cpt, y=happy_regions$happiness_score))+ 
  geom_point(aes(color = happy_regions$region)) +
  geom_smooth(method="lm") + 
  scale_x_continuous(limits=c(1.2, 1.58)) +
  scale_y_continuous(limits=c(5.5, 8.5)) +
  ggtitle("Happiest Region: Happiness Score & Gdp Per Capita") +
  xlab("GDP per Capita") + 
  ylab("Happiness Score") +  
  labs(colour="Region")

ggplot(sub_saharan_africa, aes(x=sub_saharan_africa$gdp_per_cpt, y=sub_saharan_africa$happiness_score))+ geom_point(aes(), color = "orange") +
  geom_smooth(method="lm") + 
  scale_y_continuous(limits=c(2.5, 6)) +
  xlab("GDP per Capita") + 
  ylab("Happiness Score") + 
  ggtitle("Sub Saharan Africa: Happiness Score & Gdp Per Capita") +
  labs(color="Sub Saharan Africa")

ggplot(happy_regions, aes(x=happy_regions$trust_corruption, y=happy_regions$happiness_score))+
  geom_point(aes(color = happy_regions$region)) +geom_smooth(method="lm") + 
  ggtitle("Happiest Regions: Happiness Score vs. Government Trust") +
  xlab("Gov. Trust") +
  ylab("Happiness Score") +
   labs(colour="Region")

Freedom has a very strong correlation with happiness in the happiest countries. However it doesn’t have as strong affect on happiness for Sub Saharan Africa…

ggplot(happy_regions, aes(x=happy_regions$freedom, y=happy_regions$happiness_score))+
  geom_point(aes(color = happy_regions$region)) +
  geom_smooth(method="lm") + 
  xlab("Freedom") + 
  ylab("Happiness Score") +
  ggtitle("Happiest Region: Happiness Score & Region") +
   labs(colour="Region")

ggplot(sub_saharan_africa, aes(x=sub_saharan_africa$freedom, y=sub_saharan_africa$happiness_score))+ 
  geom_point(aes(color = sub_saharan_africa$region))+
  geom_smooth(method="lm") + 
  xlab("Freedom") + 
  ylab("Happiness Score") +
  ggtitle("Sub-Saharan Africa: Happiness Score vs. Freedom")+
   labs(colour="Region")

Freedom is stronger correlated with happiness score for happiest region. For Sub-Saharan Africa, freedom seems not to be highly correlated with happiness score.We can confirm the weak relationship for the unhappiest region by running a correlation matrix:

cor(sub_saharan_africa$happiness_score, sub_saharan_africa$gdp_per_cpt)

## [1] 0.3454735

Basic Visualization & EDA on World Happiness Report 2015

Merve ozgul

1/31/2019